<?xml version="1.0" encoding="UTF-8" ?><xml><records><record><database name="!wdg&apos;s ref list_v8.enl" path="/Users/gray/Documents/!wdg&apos;s ref list_v8.enl">!wdg&apos;s ref list_v8.enl</database><source-app name="EndNote" version="10.0">EndNote</source-app><rec-number>2052</rec-number><ref-type name="Book Section">5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gray, Wayne D.</style></author><author><style face="normal" font="default" size="100%">Schoelles, Michael J.</style></author><author><style face="normal" font="default" size="100%">Myers, Christopher W.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Profile before optimizing: A cognitive metrics approach to workload analysis</style></title><secondary-title><style face="normal" font="default" size="100%">ACM CHI 2005 Conference on Human Factors in Computing Systems</style></secondary-title></titles><dates><year><style face="normal" font="default" size="100%">2005</style></year></dates><pub-location><style face="normal" font="default" size="100%">New York</style></pub-location><publisher><style face="normal" font="default" size="100%">ACM Press</style></publisher><abstract><style face="normal" font="default" size="100%">The Intelligence Analyst (IA) community will soon be the designated users of many new software tools. In the multitasking world of the IA, any one tool cannot be permitted to greedily consume cognitive resources. This situation requires a new approach to usability assessment; one that profiles the moment-by-moment demands placed on embodied cognition by a given software tool during task performance. The approach we have taken relies on families of cognitive models that interleave cognition, perception, and action at the 1/3 to 3 sec timescale. This is the level of analysis where embodied cognition forms interactive routines that adapt to the cost-benefit structure of the software tool. Our proof-of-concept is a model that performs a task that the IAs find challenging. From the trace of the model, we derive a cognitive metrics profile that pinpoints dynamic changes in workload demands on human cognitive, perceptual, or action systems.</style></abstract><notes><style face="normal" font="Times" size="100%">This research has been funded in part by contract # MDA-904-03-C-0408 to Booz Allen Hamilton from the   Advanced Research and Development Activity, Novel Intelligence from Massive Data Program.&#xD;ARDA NIMD</style></notes><urls><pdf-urls><url><style face="normal" font="default" size="100%">file://localhost/Users/gray/06%20Writings/!!archive-06%20Writings/!!ARCHIVES%20%C6%92/2005%20archive/05-CHI05/09%20pblshd_vrsn/GraySchoMyer_LBR-677.pdf</style></url><url><style face="normal" font="default" size="100%">internal-pdf://GSM05_CHI-2500828160/GSM05_CHI.pdf</style></url></pdf-urls></urls><research-notes><style face="normal" font="default" size="100%">ARDA</style></research-notes></record></records></xml>